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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

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BINARY SEQUENCES WITH A LOW CORRELATION VIA CYCLOTOMIC FUNCTION FIELDS WITH ODD CHARACTERISTICS

arXiv

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arXiv 2022年

作者： Jin, Lingfei Ma, Liming Xing, Chaoping Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Mathematical Sciences University of Science and Technology of China Hefei230026 China School of Electronic Information and Electric Engineering Shanghai Jiao Tong University 200240 China

Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths where binary sequences attracts great attention. As a result, various constructions of good binary sequences have been proposed. However, most of the known constructions made use of the multiplicative cyclic group structure of finite field Fpn for a prime p and a positive integer n. In fact, all pn + 1 rational places including the place at infinity of the rational function field over Fpn form a cyclic structure under an automorphism of order pn + 1. In this paper, we make use of this cyclic structure to provide an explicit construction of binary sequences with a low correlation of length pn +1 via cyclotomic function fields over Fpn for any odd prime p. Each family of binary sequences has size pn − 2 and its correlation is upper bounded by 4 + ⌊2 · pn/2⌋. To the best of our knowledge, this is the first construction of binary sequences with a low correlation of length pn + 1 for odd prime p. Moreover, our sequences can be constructed explicitly and have competitive parameters. © 2022, CC BY.

关键词： Binary sequences

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Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification

arXiv

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arXiv 2022年

作者： Hou, Junlin Xu, Jilan Zhang, Nan Zhang, Yuejie Zhang, Xiaobo Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Children’s Hospital of Fudan University National Children’s Medical Center Shanghai China

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifically, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%. © 2022, CC BY.

关键词： COVID-19

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No-Reference Image Quality Assessment Based on Active Reasoning Module

No-Reference Image Quality Assessment Based on Active Reason...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Junwei Qi Yuhao Deng Qingchun Wang Zhen Yang Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computer Science and Technology Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately.

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Scale-aware network with regional and semantic attentions for crowd counting under cluttered background

arXiv

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arXiv 2021年

作者： Yi, Qiaosi Liu, Yunxing Jiang, Aiwen Li, Juncheng Mei, Kangfu Wang, Mingwen Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China School of Computer and Information Engineering Jiangxi Normal University Nanchang China Department of Mathematics The Chinese University of Hong Kong [ShengZheng ShengZheng China

Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current research, the accuracy of counting numbers and crowd density estimation are the main concerns. Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge. In order to solve this problem, we propose a Scale-Aware Crowd Counting Network (SACCN) with regional and semantic attentions. The proposed SACCN distinguishes crowd and background by applying regional and semantic self-attention mechanisms on the shallow layers and deep layers, respectively. Moreover, the asymmetric multi-scale module (AMM) is proposed to deal with the problem of scale diversity, and regional attention based dense connections and skip connections are designed to alleviate the variations on crowd scales. Extensive experimental results on multiple public benchmarks demonstrate that our proposed SACCN achieves satisfied superior performances and outperform most state-of-the-art methods. All codes and pre-trained models will be released soon. © 2021, CC BY.

关键词： Semantics

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A semi-informative aware approach using topic model for medical search

A semi-informative aware approach using topic model for medi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qinmin Vivian Hu Liang He Mingyao Li Jimmy Xiangji Huang E. Mark Haacke Shanghai Key Laboratory of Multidimensional Information Processing MR Research Facility Wayne State University Detroit MI USA Department of Computer Science & Technology East China Normal University Shanghai China Information Retrieval and Knowledge Management Research Lab York University Toronto Canada

We propose a semi-informative aware approach using the topic model on query expansion problem in the biomedicine domain. the demographics and disease information is applied to semi-structure the topic model as the “known” label, compared to the traditional latent topics in topic modelling. Then, we suggest to select three terms from the top ranked documents to expand the query, based on the assumption in the pseudo relevance feedback method that the top ranked results in the first retrieval around are relevant. After that, we conduct the experiments on the TREC medical records data sets with extensive analysis and discussions. Numerically, we achieve the improvements of 7.41% on MAP, 9.29% on Bpref and 5.60% on P@10 respectively over the strong baselines.

关键词： Indexes Diseases information retrieval Numerical models Biological system modeling Educational institutions Mathematical model

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Named Entity Driven Zero-Shot Image Manipulation

Named Entity Driven Zero-Shot Image Manipulation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhida Feng Li Chen Jing Tian JiaXiang Liu Shikun Feng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Baidu Inc. Institute of Systems Science National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduced StyleEntity, a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference, all within a single training phase. Additionally, we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference, effectively eliminating the noise directions, thus achieving stable manipulation. In our experiments, StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code, model weights, and datasets are available at https://***/feng-zhida/StyleEntity.

关键词： Training Adaptation models computer vision Codes Computational modeling Noise Stability analysis

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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An Efficient Weakly-Supervised Learning Method for Optic Disc Segmentation

An Efficient Weakly-Supervised Learning Method for Optic Dis...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Wen Leiting Chen Lifeng Qiao Chuan Zhou Shuo Xi Rui Guo Yu Deng Key Laboratory of Digital Media Technology of Sichuan Province School of Computer Science and Engineering Institute of Electronic and Information Engineering in Guangdong School of Medicine University of Electronic Science and Technology of China Sichuan Provincial People’s Hospital King’s College London London UK

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Accurate optic disc segmentation plays an essential role in the early diagnosis of glaucoma, which has been a major cause of irreversible blindness for the past decade. Recently, U-shape Convolutional Neural Network (CNN) models have achieved favourable performance in optic disc segmentation. However, it is worth noting that these models require a large number of pixel-level annotations while these annotations are difficult to obtain in clinical practice. As a solution, weakly-supervised training methods are commonly implemented, but it will provoke U-shape CNN generating inaccurate, diluted, and grid-like segmentation results. In this paper, we propose a novel Hybrid Network (HyNet) to solve the issue above. HyNet consists of a U-shape backbone hybridized with a cross-scale connection structure, which makes better use of multi-scale visual semantics. Nevertheless, the generalization ability of HyNet is affected by the domain shift among different datasets. Therefore, we innovatively combine weakly- and fully-supervised training methods, namely Hybrid Process (HyProcess), to solve the domain shift problem. Experimental results on ONHSD, DRIONS-DB, and DRISHTI-GS datasets show that our model outperforms the state-of-the-art, reaching Dice of 82.39(%), 93.72(%), and 95.34(%) respectively. Additionally, our ablation study validates the effectiveness of HyNet along with HyProcess, and further analysis reflects their value in clinical practice.

关键词： Image segmentation Training Annotations Visualization Optical imaging Semantics Interpolation

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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